User behavior influence in transportation systems

ABSTRACT

Systems and methods described receive a request for a transportation plan for a user, wherein the request comprises a starting point and an ending point for a route. Systems and methods then generate a set of potential transportation plans for the route, determine an impact of a subset of plans in the set of potential transportation plans and estimate a likelihood of acceptance of the subset of plans in the set of potential transportation plans based on a personal preference model for the user. Systems and methods also select an influence strategy of the user based on a user model and generate a message recommending a selected plan from the subset of plans for the user, wherein the message is generated based on the selected influence strategy.

TECHNICAL FIELD

Implementations of the present disclosure relate to planning andimplementation of transportation routes.

BACKGROUND

The use of systems to generate transportation plans from one location toanother can enable users to navigate in unknown areas without the needfor a map, asking for directions, knowledge of local transportationoptions, or planning ahead. However, such transportation plans may notbe optimal for user preferences or the transportation system and usersmay not adopt more beneficial transportation plans.

BRIEF DESCRIPTION OF THE DRAWINGS

The described embodiments and the advantages thereof may best beunderstood by reference to the following description taken inconjunction with the accompanying drawings. These drawings in no waylimit any changes in form and detail that may be made to the describedembodiments by one skilled in the art without departing from the spiritand scope of the described embodiments.

FIG. 1 is a schematic diagram of an embodiment of systems to generate atransportations plan, which can be used in accordance with someembodiments.

FIG. 2 is a schematic diagram of an embodiment of a user model todetermine transportation plans and messaging, which can be used inaccordance with some embodiments.

FIG. 3 is a user interface including a transportation plan, which can beanalyzed in accordance with some embodiments.

FIG. 4 is a user interface including a transportation plan, which can beanalyzed in accordance with some embodiments.

FIG. 5 is a flow diagram of an embodiment of a method of generatingtransportation plan, in accordance with some embodiments.

FIG. 6 is an illustration showing an example computing device which mayimplement the embodiments described herein.

DETAILED DESCRIPTION

Described herein are navigation systems providing recommended routesthat accomplish a goal of a transportation system and are provided tocommuters and travelers based on user preferences. While systems canprovide optimized routes for individuals, those recommendations mayimpact a transportation system or goal of a stakeholder of thattransportation system. Accordingly, other transportation plans may havebenefits for the transportation system or a particular user. However,providing transportation plans that may improve a goal of atransportation system may result in recommending plans to a user thatthe user is unlikely to accept. Systems and method described hereinselect plans that improve an application goal, but also generatemessages to persuade a user to accept the provided recommendation. Thus,systems and methods determine a likelihood of acceptance of varioustransportation plans for a user based on a user model of that user. Thesystem then selects a transportation plan to provide to the user basedon the likelihood of acceptance of the plan and generates a message toinfluence acceptance by the user.

In some embodiments, a selected plan may be provided to a user with amessage selected to improve the potential acceptance by the user. Forexample, certain plans may contribute to one or more goals that mayinfluence particular users. The system may determine, based on a usermodel, a plan most likely to be accepted as well as messaging directedto that user. For example, certain users may respond better or worse tomessaging involving reciprocity, social proof (conformity), commitment,liking, authority, scarcity, or unity messaging. Messages may then bedesigned for a particular user based on available transportation plansand personal preferences of the user.

In some embodiments, a transportation analysis system may determinepotential transportation plans and influence a user to accept one suchplan using a variety of factors. The transportation analysis system mayuse static context, dynamic contexts, personal preference,influenceability, application goals, or a combination thereof toinfluence a user. Static context factors include factors that don'tchange such as available options and the convenience and desirability ofthose options. For example, a multi-modal transportation system mayinclude a number of public transportations options as well as one ormore transportations options that are available to individuals. Dynamiccontext factors include factors that change or influence the convenienceor desirability of travel options. For example, changing weatherconditions may affect whether or not walking or biking transportationmodes are readily available to a user at a specific time. Personalpreferences include those preferences about an individual. These caninclude preferences for particular modes of travel compared to others,as well as the importance of arriving at a particular time, leaving at aparticular time, making stops along the way, or other personalpreferences of a user. The personal preferences may be generated throughsurvey data or observations of a user's response to suggested routes.Influenceability factors include identification of strategies that aperson is susceptible to and what degree. Although other models can beused to determine a user's infuenceability, in some embodiments, thepersonal preference factors may include reciprocity, social proof,commitment, liking, authority, scarcity, unity or other influencedimension factors. Application goals can measure the value of atransportation plan to a transportation system or a stakeholder thereof.For example, a stakeholder may attempt to reduce emissions, increaserevenue, reduce congestion, increase traffic to commercial areas, or thelike. A transportation analysis system may use these combination offactors to determine a transportation plan that improves an applicationgoal, is practical to a user, and is likely to be accepted by a user.

In some embodiments, systems and method described herein combine usermodels of transportation preferences, multi-modal transportation models,and energy models to identify routes that are acceptable to individualsthat save energy and time, and therefore money across the entiretransportation network. Some embodiments incorporate transportationsystem modeling with individual user impact modeling, based upon theirexplicit, implicit, or other expressed needs to generate personalizedmulti-modal transportation recommendations. Multi-modal transportationsystem include a variety of types of transportation means to get fromone place to another within a geographic area. Multi-providertransportation systems incorporate different private providers intosingle plans. Impact modeling can capture a wide range of factors thatmight be important, favored, or otherwise have a preference to a user.In some embodiments, the system modeling captures the effects of thosedecisions on other travelers. In some embodiments, the systems andmethods described can be used as mobility-as-a-service providers such asride sharing services, mobility-as-a-service infrastructure services,transportation demand management automakers, smart city services,transportation departments, such as transit authorities or the like, orother users monitoring usage of transportation systems. In embodiments,the systems and methods described can be used by third-party companiesand/or consumer businesses.

To generate a personal preference model for a user, the transportationanalysis system can use both implicit and explicit data regarding theuser. Explicit data can include a user survey or other provided datafrom the user. For example, the user may indicate they do not own abike, cannot walk to work, prefer to use public transportation or thelike. This data can rule out certain routes or modes of transportationas not being acceptable to the user. Implicit data can include dataderived from other data sources. For example, the user may have healthmonitoring that indicates a low level of activity and increasingactivity levels may be built into the user's profile. In anotherexample, crime information may be used to identify areas of increasedcrime activity and rule out certain routes or modes of transportation.The transportation analysis system may also use calendar events, pastacceptance of routes, job titles, or the like to generate a personalpreference profile. The transportation analysis system may also useroute ratings by a user as an input to update the preferences of a userin an associated user profile.

By modeling all modes of transportation in terms of a transportationanalysis system can optimize one or more features, such as travel time,traffic levels, fuel consumption, comfort, or others factors thatcontribute to an application goal. In some embodiments, transportationplans may be combined with other dynamic contextual conditions that makeone more or less preferable over another, such as traffic density andslowdowns, weather, importance of arriving on time and so on. In someembodiments, static or dynamic contexts may be received from third partydata sources in real-time or near real-time. Because each potentialtransportation plan impacts other travelers both positively andnegatively, the transportation analysis system an estimate the impact ofa transportation plan including time, congestion, fuel, carpool options,or other impacts on a transportation system or geographic area.Accordingly, the transportation analysis system can select from a set ofpotential transportation plans that improve an application goal of astakeholder in the transportation system as a whole.

In some embodiments, estimating the impact on the transportation systemincludes simulation of the transportation system. The simulation can bebased on a model of the transportation system that includes a number ofconnected nodes and edges. The nodes can represent intersections ofroads and connections between different modes of transportation. Theedges can represent the distance, time, or energy consumption withinthose edges. In some embodiments, the simulation can determine an amountof time or energy consumed by the user or other travelers in thetransportation based on the model. For example, if the user decides totravel and the travel includes time spent at an intersection, thetransportation analysis system can simulate the impact based ondetermination of the additional time and fuel consumption generated bythe increase in the number of acceleration and deceleration maneuvers ofother vehicles that wait on the user.

By modeling each of the dimensions that may influence a user inselection of a transportation plan, the transportation analysis systemcan develop a highly targeted means of influencing an individual to makedifferent transportation choices including mode, route, additionalstops, and departure time based on application objectives (e.g.,reducing energy, reducing congestion, reducing emissions, increasingstore revenue, or the like). A user model can then be used to targetmessages to a user that are more likely to influence the user. Forexample, a user may be more influenced by how much a decision helps theenvironment. Accordingly, the transportation analysis system maygenerate a message indicating an amount of pollution that is reduced.Other users may be influenced by reducing congestion, monetary savings,or other messaging to influence to users.

In some embodiments, one or more factors or models may be generated by amachine learning system or be a machine learning model. For example, auser model may include a machine learning model that predicts aninfluence strategy for the user based on information known about theuser. A machine learning system may input user demographic data, surveydata, implicit data from third party sources, previous user responses,or the like to generate an output of a predicted influence strategy forthe user, for instance. In some embodiments, a machine learning systemmay also update a machine learning model for a user based on trainingfrom subsequent responses to influence strategies. Other machinelearning models may be used to define other factors when selectingroutes and messaging strategies for a user.

FIG. 1 is a diagram showing a transportation analysis system 100 thatgenerates a transportation plan from a first location to a secondlocation. The transportation analysis system 100 also provide plans thattake into account user preferences and the likelihood that a user willaccept a suggested transportation plan based on those preferences andmessaging directed to influencing the user. The transportation analysissystem 100 can receive a request for a transportation plan from a userdevice 160 or another device associated with the user. The request canbe an explicit request for a transportation plan or can be inferredbased on a routine that is followed by a user. The transportationanalysis system 100 can respond to the request by generating a set oftransportation plans and selecting a preferred plan to provide to theuser device 160. In some embodiments, the transportation analysis system100 may use additional resources such as remote data sources 105 andtransportation system data 115 to generate the transportation plan ordetermine user preferences and influence patterns. After a plan isselected by the transportation analysis system 100, it can be providedback to the user device 160. The selected plan may account for anoverall impact of the user, the impact of the selected plan on anoverall transportation system, and the likelihood of the user to acceptthe recommendation.

In some embodiments, the transportation analysis system 100 can includea user model generator 110, a transportation system optimization service130, a transportation plan generator 120, a transportation plan selector140 and a user interface generator 150. The user model generator 110generates a user model 135 for a user that includes user preferences anda user influence model including messaging recommendations forinfluencing the user. The user model generator 110 may also useadditional remote data sources 105. The transportation plan generator120 can generate a number of transportation plans that are available totravel from the first location to the second location. For example, thetransportation plan generator 120 may user transportation system data115 or a transportation system model 125 that provides a model ofavailable routes and modes of transport within the transportationsystem. The transportation plan generator 120 can then evaluatepotential transportation plans to generate a number of plans forevaluation by a transportation system optimization service 130 ortransportation plan selector 140. For example, the transportation systemoptimization service 130 may determine how a transportation plan affectsone or more applications goals of the transportation system. In someembodiments, the transportation system optimization service 130 mayselect a subset of plans that have a positive impact on one or moreapplications goals as well as potential user preferences ortransportation modes to which a user has access to or is willing to use.

The transportation analysis system 100 may also include a transportationplan selector 140 that selects a plan based on one or more of the usermodel 135 of the user and estimated impacts on the transportationsystem. The select plan can then be provided to the user device 160through a user interface generator 150. Based on the selected plan, theuser can choose whether or not to accept the selected plan. If the userselects the transportation plan, a navigation system 165 of the userdevice 160 can use the plan to direct the user to the requesteddestination.

The user model generator 110 of the transportation analysis system 100can generate a model based on various types of data. For example, theuser model generator 110 can user responses to a set of questionsprovided to a user. The user may provide available transportation modesthat the user has, preferred modes of transportation, fitness level,whether they work on commutes, or the like. The user model 135 can alsoinclude preferences derived from actions of the user. For example, theuser preferences 135 may be updated based upon responses of the userdevice 160 to proposed transportation plans. In addition, the userpreferences 135 can generate preference from remote data sources 105that pertain to the user. An example remote data source 105 can includea fitness tracker that counts steps for the user. If there is a changein the activity of the user, the user preferences 135 can be updated toindicate the user may want to exercise more. Other remote data sources105 may include social networks of the user, preferences of users withsimilar demographics of the user, or other information provided by thirdparty sources.

The user model 135 also includes an indication of a user'ssusceptibility to influence based on recommendations from thetransportation analysis system 100. For example, certain users may bemodeled as more likely to accept recommendations that differ from anormal commute than others. Additionally, the user model 135 may includea model of the likely types of influence that may impact that user more.For example, certain users may respond better to environmental concerns,while others may care more about time, congestion, or other factors.

The user model 135 can then be used by the transportation plan selector110. For example, the user model 110 may provide weights or models fordifferent factors that are preferred by a user. Accordingly, thetransportation plan selector 140 may provide an estimated likelihood ofaccepting a transportation plan based on user preferences and userinfluenceability. In some embodiments, the transportation plan selector140 can then select a transportation plan to provide to the user device160 based on the likelihood of acceptance and impact on applicationgoals of the transportation analysis system 100. FIG. 2 describesadditional details of an example user model 135, which may be used inaccordance with implementations of the disclosure.

In some embodiments, the transportation plan generator 120 generatesmulti-modal transportations plans for the requested destination based onthe transportation system model 125. The transportation system model 125can include a virtual map of nodes and edges in a transportation system.The transportation system may be a particular geographic area or an areawithin which the locations of the requested transportation plan arelocated. The transportation system model 125 may include an amount oftime taken to travel across an edge during different times of day anddays of the week. The transportation system model 125 may also includecongestion levels, average energy consumption for a transport across anedge, wait times at particular nodes, types of transfers at differentnodes (e.g., which edges a traveler can move to at one node depending onwhich edge the traveler approached from), and the like. In someembodiments, other types of transportation system models may be used togenerate transportation plans.

The transportation plan generator 120 then determines a set of potentialtransportation plans for the user to travel from one location to thedesired location. In some embodiments the transportation plan generator120 may generate plans that overlay the nodes and edges of thetransportation system model 125. The transportation plan generator 120may consider only the portions of the transportation system model 125that have modes available to the user. For example, if the userpreferences 135 indicate that the user cannot ride a bike, thetransportation plan generator 120 may ignore those edges that require abike to be practical. The transportation plan generator 120 may generatea number of potential plans as candidates for consideration by thetransportation system optimization service 130 and transportation planselector 140. In some embodiments, the request for a transportation planmay be received with time to generate, hundreds, thousands, tens ofthousands, or more transportation plans. The transportation analysissystem 100 can consider these plans impacts of the plans on applicationgoals of the transportation system and a user's likelihood of acceptingthe plan.

In some embodiments, a transportation system optimization service 130simulates one or more of the potential plans generated by thetransportation plan generator 120. The simulation can provide estimatedtravel time, energy consumption, fuel costs, congestion, impact onrevenue of local economies, or the like. These can be generated throughthe simulation for the user as well as the marginal travel time, energyconsumption, fuel costs, congestion, or the like that is added to thetransportation system as a whole based on the addition of the travel bythe user. For example, for each transportation plan generated by thetransportation plan generator 120, the transportation systemoptimization service 130 can simulate the time and energy consumed ateach edge and node of a transportation system model. Those consumptionlevels can be added up for the user. In addition, the total time,energy, and congestion of the transportation system with the potentialtransportation plan can be compared to a total of the transportationsystem without the user. Accordingly, the comparison provides adifference in the efficiency of the transportation system for differenttransportation plans based on these comparisons. In some embodiments,the transportation system simulator 130 additionally uses transportationsystem data 115. The transportation system data 115 can provide updatedinformation about traffic, weather, public transportation schedules, orthe like to the transportation system optimization service 130. In someembodiments, the transportation systems data 115 can be pulled from anumber of sources across a network. In some embodiments, thetransportation system data 115 can also be part of remote data sources105.

The transportation plan selector 140 can select one or more plans basedon impact on application goals of the transportation system and thelikelihood of a message influencing the user to accept thattransportation plan. For example, the transportation plan selector 140can compare the plans to determine those with an impact score over athreshold and with a positive (or least negative) estimated impact onthe transportation system. In some embodiments, the transportation planselector 140 may select those potential plans that have the highestpercentile impact score compared to the other potential plans. Thetransportation plan selector 140 may also only consider thosetransportation plans with a highest percentile estimated impact on thetransportation system. In some embodiments, the impact scores and theestimated impact on the transportation system may be combined alikelihood of a message influencing the user to accept thattransportation plan to select a recommended transportation plan.

The user interface generator 150 can provide the selected plan to theuser device 160 for the user to decide whether to use the plan. In someembodiments, more than one selected plan may be provided to the userdevice 160 with an indication of costs and benefits of each of theselected plans. The user interface generator 150 may communicate with auser device 160 through text messaging, email, a dedicated app, anavigation system 165, or other communication techniques. Furthermore,the user interface generator 150 can use data from the user model 135 todetermine an influence strategy for contacting the user. For example,the user model 135 may rank the likelihood of different strategies forinfluencing the user. In some embodiments, the rankings may be based onimplicit or explicit user data. Furthermore, the user model 135 may betrained based on user responses to update the ranking of variousinfluence strategies. For example, the user model may recommend acertain type of message to contact the user, but over time the user maynot accept transportation plans based on that type of messaging.Accordingly, the user model may update the ranking of messaging types totry other messages to influence the users.

In addition to general influence types, the user model 135 may includeindications of the type of applications goals to which a user is moresusceptible. For example, if a user indicates that conservation isimportant to them, then messaging directed toward reduced fuelconsumption may be used more often in messages to influence the user. Invarious embodiments, other influence strategies may be used alone or incombination to influence the impact of messaging on a user.

In some embodiments, to generate a message, the user interface generator150 may user one or more template messages for contacting the userdevice 160. For example, the messaging may include an application goalthat is improved by use of the transportation plan as well as anindication of why the user should use the plan. For example, if the useris heavily influence by authority, a template message saying that a citymayor recommends the plan to help the city with congestion may be used.If the user is heavily influenced by community, the messaging mayinclude a message describing how many people in the community are usinga particular mode or route for transportation. Additional templatemessaging may accompany additional influence strategies. Example userinterfaces are shown in FIGS. 3-6 below.

The user device 160 can be a user computing device such as a smartphone,personal computer, GPS system, in-vehicle navigation system, or anothersystem. In some embodiments, the transportation analysis system 100 maybe executed on user device 160. For example, the transportation analysissystem 100 may operate using hardware resources of the user device 160.In some embodiments, the transportation analysis system 100 can operateon a host server separate from the user device. Upon receiving aselected transportation plan, the user device 160 can provide a userinterface to the user. The user device then receives an indication ofwhether to proceed with the recommended transportation plan. In someembodiments, the user device 160 can then provide the transportationplan to a navigation system 165 and follow directions on the user device160. In some embodiments, if the user does not accept the proposedroute, the transportation analysis system 100 can provide alternative orother routes to the user device. In some embodiments, the user device160 may revert to another navigation system 165 in response to decliningthe selected transportation plan.

FIG. 2 is an example of a user model 200 according to someimplementations of the present disclosure. For example, the user model200 may be the same or similar to a user model 135 as described withreference to FIG. 1 above. As shown, user model 200 includes travelpreferences 202, static context 204, dynamic context 206, and aninfluence model 208, however, in some embodiments, a user model mayinclude fewer or additional types of models. For example, dynamiccontext 206 may not be part of a user model, but may be used by atransportation plan generator.

In some embodiments, travel preferences 202 may include types of travelavailable to the user, preferences between those types of travel,desired times of travel, or other information about the user's travelpreferences. The travel preferences may be used to determine availabletransportation plans based on available transportation. In addition, thetravel preferences 202 may be used to determine which plans a user islikely to choose over others based on the preferred travel modes ortimes. The travel preferences 202 can also be used to generate messagingto influence the user. For example, a message may be created thatrecommends a transportation plan that avoids less preferredtransportation modes.

Static context 204 can include additional information about a user andthe user's environment. For example, static context 204 can includesurvey results from a user, prior history of accepting transportationplans, demographic information of the user, details of a transportationsystem surrounding the user, contact information of the user, oradditional information. The static context data 204 can be used torefine transportation plans, model additional characteristics of a user,generate influence models or messaging for the user, or the like. Forexample, the static context 204 of a user can be used to generateinfluence model 208. Accordingly, static context 204 can be utilized togenerate messages to improve acceptance of transportation plans for theuser.

Dynamic context 206 includes changing information or a user's reactionto changing information. For example, dynamic context 206 can includeweather and a user's preferred transportation modes in certain weatherconditions. Dynamic context can also include congestion in atransportation system, availability of rideshare or other transportationmodes with variable schedules or availabilities, or other informationregarding changing conditions within a transportation system. Thedynamic context 206 can be used for transportation plan generation,selection, or influence. For example, the dynamic context 206 can beused to determine available transportation plans or selection of atransportation plan to meet a user's preferences. In some embodiments,the dynamic context 206 can also be used to generate messaging based onthe user's preferences. For example, if a user prefers to take the trainwhen it is raining, a messaging component of a transportation analysissystem may provide a message telling the user to use a transportationplan including a train route because it is raining. Accordingly, thedynamic context 206 can be used to influence users.

An influence model 208 of the user model 200 may provide a model thatpredicts the most influential modes of persuasion of a user. In someembodiments, the influence model 208 may be a machine learning modelthat predicts a user's influenceability based one or more of travelpreferences 202, static context 204, dynamic context 206, or otherinformation. For example, an influence model 208 may be generated by auser's answer to a number of survey questions. In some embodiments, theinfluence model 208 may also be dynamically updated based on feedbackfrom a user. For example, if a type of influence messaging is rejectedby a user, the influence model 208 may be updated to favor that type ofmessaging for persuasion in future messages.

FIG. 3 is an example user interface 300 that may be provided on a userdevice according to aspects of the disclosure. In some embodiments, theuser interface 300 may be provided on a user device as discussed withreference to FIG. 1. For example, the user interface 300, or datapopulating the user interface 300, may be provided by a user interfacegenerator 150 of a transportation analysis system 100 as described withreference to FIG. 1. Although shown with particular features, the userinterfaces shown in FIG. 3 may include fewer, additional, or differentfeatures than those that are shown. For example, user interface 200 maynot include profile information of the user.

The user interface 300 includes a description 310 of a user profile ofthe user. In some embodiments, the user may update their profile withadditional information. A transportation analysis system can then updatea user model for the user accordingly. For example, if a user changespreferences, modes of available transportation, or the like, a usermodel (such as user model 200 in FIG. 2) can be updated accordingly by auser model generator. The user interface also includes a map 320 showingthe user an alternative transportation plan. The map 320 includes anormal route for the user as well as an alternative trip. Thealternative trip may be provided to the user based on a selectedtransportation plan generated by a transportation analysis system asdescribed with reference to FIG. 1. For example, the transportation planmay have been selected in response to a determination of the likelihoodof acceptance by the user.

In addition to the map, the user interface 300 may include a description330 of the changes to the transportation plan and the estimated impactof those changes. As shown in FIG. 3, the change to taking the metroreduces emissions compared to the normal transportation methods. Themessaging may be selected based on an influence model of the user. Forexample, John may have a model indicating that he is interested in hisimpact on the environment and world around. Accordingly, a messagingsystem can use a template an insert emissions savings attributable tothe change in his activity. For instance, a transportation analysissystem may insert values for the emission savings and savings for theyear based on previous route changes by the user. If a differentinfluence model was generated for the user, the same route may beselected with different messaging. For example, if a user is more likelyto be influenced by social proof, messaging may suggest that a certainnumber of people ride the metro everyday as part of the way to improvethe area in which they live. In some embodiments, the user interface 300may also include one or more controls for the user to accept or declinethe transportation plan provided.

FIG. 4 is an example user interface 400 that may be provided on a userdevice according to aspects of the disclosure. In some embodiments, theuser interface 400 may be provided on a user device, for example asdiscussed with reference to FIG. 1. For example, the user interface 400,or data populating the user interface 400, may be provided by a userinterface generator 150 of a transportation analysis system 100 asdescribed with reference to FIG. 1. Although shown with particularfeatures, the user interface shown in FIG. 4 may include fewer,additional, or different features than those that are shown. Forexample, user interface 400 may not include profile information of theuser.

The user interface 400 includes a description 410 of a user profile ofthe user. In some embodiments, the user may update their profile withadditional information. A transportation analysis system can then updatea user model for the user accordingly. For example, if a user changespreferences, modes of available transportation, or the like, a usermodel (such as user model 200 in FIG. 2) can be updated accordingly by auser model generator. The user interface also includes a map 420 showingthe user selected transportation plan. In some embodiments, the map 420shows the normal route taken by the user.

The user interface includes a message 430 designed to influence the userto use the selected transportation plan. As shown, the message describeschanging the driving speed of the user based on the user's drivinghabits. The recommendation is to accept the driving speeds of thenavigation system. The description shows the benefit of the slowerspeeds to the user. This may be determined based on comparison of apotential transportation plan to the normal transportation of the user.The message 430 may be selected based on an influence model for theuser. For example, an influence model for the user may indicate that theuser is conscious of time and select a message template showing abenefit received with a small cost. Accordingly, the generated message430 may be selected to show improvements to fuel economy as well asindicate to the user that there will not be a change in time. In someembodiments, the user interface 400 may also include one or morecontrols for the user to accept or decline the transportation planprovided.

FIGS. 3 and 4 provide example user interfaces that are generated toimprove acceptance of transportation plans that fulfil one or moreapplications goals of a stakeholder of a transportation system. Forexample, the application goals can include reducing congestion, reducingemissions, increasing revenue from businesses in certain areas, or thelike. While shown with particular messaging for example users, atransportation analysis system may user a variety of messages toinfluence particular users of a transportation system. Accordingly,FIGS. 3 and 4 are non-limiting examples of potential user interfaceswhich may be provided to one or more users.

FIG. 5 is a flowchart 500 illustrating example operations of atransportation analysis system. For example, the processes describedwith reference to FIG. 5 may be performed by a transportation analysissystem 100 as described with reference to FIG. 1. Beginning at block510, the transportation analysis system receives a request for atransportation plan for a user. The request may include a starting pointand ending point of a route that is requested. In some embodiments, therequest may be given explicitly by a user, or it may be predicted basedon a routine of the user.

In block 520, the transportation analysis system generates a set ofpotential transportation plans for the user. The transportation plansmay be based on a transportation system model for the area the user islocated. In some embodiments, the potential transportation plans may belimited to modes of transportation that are available to a particularuser. In some embodiments, the transportation plans may be generated bya transportation plan generator 120 as described with reference to FIG.1.

In block 530, the transportation analysis system determines an impactfor at least a first subset of plans in the set of potentialtransportation plans. For example, the impact may be generated based onone or more application goals of the transportation analysis system. Theimpact may include transportation time of the user, emissions of theuser, congestion of the transportation system traffic passing certainlocations, overall impact on a transportation system, or other impactmeasures. In some embodiments, the estimations may be generated throughsimulation of the transportation plans within the transportation systemmodel. In some embodiments, the estimations for the plan may begenerated based on a combination of user models and application goals.

In block 540, the transportation analysis system estimates a likelihoodof acceptance of the subset of plans in the set of potentialtransportation plans based on a personal preference model for the user.For example, the transportation analysis system may determine thatcertain transportation plans are not likely to be accepted by the userbased on personal preferences of the user in a user model. Thetransportation analysis system may also use static and dynamic contextsto determine which transportation plans are most likely to be acceptedby the user.

In block 550, the transportation analysis system selects an influencestrategy for the user. For example, the influence strategy may be basedon an influence model of the user. The influence model may indicatewhich of a number of influence strategies are most likely to persuadethe user to accept a transportation plan. For example, differentinfluence strategies may be targeted to a user based on influenceabilitydimensions of the user. In some embodiments, these dimensions mayinclude reciprocity, social proof (conformity), commitment, liking,authority, scarcity, and unity (the more we identify with others, themore we are influenced by them), although, other embodiments may useother influence strategies based on priorities of the user. For example,other influence strategies may include priorities such as time, money,comfort, or the like. In some embodiments, an influence model may be amachine learning process that predicts a best influence model for theuser based on information known about the user. In some embodiments, theinfluence model may be updated over time in response to a user acceptingor rejecting transportation plans with particular influence messaging.

In block 560, the transportation analysis system generates a messagerecommending a selected plan from the subset of plans for the user,wherein the message is generated based on the selected influencestrategy. For example, in some embodiments, a user interface generatormay access a database of messaging templates and select a template thatmatches the influence strategy selected. The transportation analysissystem can then determine based on one or more impact determinationappropriate details to fill in the message template. For example, if amessage is directed to a user that is influenced by reciprocity, themessage may indicate the savings the user will receive in gas money fortaking a slower route. In some embodiments, a transportation analysissystem may generate messages in other manners, such as natural languageprocessing or machine learning systems.

Various operations are described as multiple discrete operations, inturn, in a manner that is most helpful in understanding the presentdisclosure, however, the order of description may not be construed toimply that these operations are necessarily order dependent. Inparticular, these operations need not be performed in the order ofpresentation.

FIG. 6 illustrates a diagrammatic representation of a machine in theexample form of a computer system 600 within which a set ofinstructions, for causing the machine to perform any one or more of themethodologies discussed herein, may be executed. In alternativeembodiments, the machine may be connected (e.g., networked) to othermachines in a local area network (LAN), an intranet, an extranet, or theInternet. The machine may operate in the capacity of a server or aclient machine in a client-server network environment, or as a peermachine in a peer-to-peer (or distributed) network environment. Themachine may be a personal computer (PC), a tablet PC, a set-top box(STB), a Personal Digital Assistant (PDA), a cellular telephone, a webappliance, a server, a network router, a switch or bridge, a hub, anaccess point, a network access control device, or any machine capable ofexecuting a set of instructions (sequential or otherwise) that specifyactions to be taken by that machine. Further, while only a singlemachine is illustrated, the term “machine” shall also be taken toinclude any collection of machines that individually or jointly executea set (or multiple sets) of instructions to perform any one or more ofthe methodologies discussed herein. In one embodiment, computer system600 may be representative of a server computer system, such astransportation analysis system 100.

The exemplary computer system 600 includes a processing device 602, amain memory 604 (e.g., read-only memory (ROM), flash memory, dynamicrandom access memory (DRAM), a static memory 606 (e.g., flash memory,static random access memory (SRAM), etc.), and a data storage device618, which communicate with each other via a bus 630. Any of the signalsprovided over various buses described herein may be time multiplexedwith other signals and provided over one or more common buses.Additionally, the interconnection between circuit components or blocksmay be shown as buses or as single signal lines. Each of the buses mayalternatively be one or more single signal lines and each of the singlesignal lines may alternatively be buses.

Processing device 602 represents one or more general-purpose processingdevices such as a microprocessor, central processing unit, or the like.More particularly, the processing device may be complex instruction setcomputing (CISC) microprocessor, reduced instruction set computer (RISC)microprocessor, very long instruction word (VLIW) microprocessor, orprocessor implementing other instruction sets, or processorsimplementing a combination of instruction sets. Processing device 602may also be one or more special-purpose processing devices such as anapplication specific integrated circuit (ASIC), a field programmablegate array (FPGA), a digital signal processor (DSP), network processor,or the like. The processing device 602 is configured to executeprocessing logic 626, which may be one example of transportationanalysis system 100 shown in FIG. 1, for performing the operations andsteps discussed herein.

The data storage device 618 may include a machine-readable storagemedium 628, on which is stored one or more set of instructions 622(e.g., software) embodying any one or more of the methodologies offunctions described herein, including instructions to cause theprocessing device 602 to execute transportation analysis system 100. Theinstructions 622 may also reside, completely or at least partially,within the main memory 604 or within the processing device 602 duringexecution thereof by the computer system 600; the main memory 604 andthe processing device 602 also constituting machine-readable storagemedia. The instructions 622 may further be transmitted or received overa network 620 via the network interface device 608.

The machine-readable storage medium 628 may also be used to storeinstructions to perform a method for analyzing log data received fromnetworked devices, as described herein. While the machine-readablestorage medium 628 is shown in an exemplary embodiment to be a singlemedium, the term “machine-readable storage medium” should be taken toinclude a single medium or multiple media (e.g., a centralized ordistributed database, or associated caches and servers) that store theone or more sets of instructions. A machine-readable medium includes anymechanism for storing information in a form (e.g., software, processingapplication) readable by a machine (e.g., a computer). Themachine-readable medium may include, but is not limited to, magneticstorage medium (e.g., floppy diskette); optical storage medium (e.g.,CD-ROM); magneto-optical storage medium; read-only memory (ROM);random-access memory (RAM); erasable programmable memory (e.g., EPROMand EEPROM); flash memory; or another type of medium suitable forstoring electronic instructions.

The preceding description sets forth numerous specific details such asexamples of specific systems, components, methods, and so forth, inorder to provide a good understanding of several embodiments of thepresent disclosure. It will be apparent to one skilled in the art,however, that at least some embodiments of the present disclosure may bepracticed without these specific details. In other instances, well-knowncomponents or methods are not described in detail or are presented insimple block diagram format in order to avoid unnecessarily obscuringthe present disclosure. Thus, the specific details set forth are merelyexemplary. Particular embodiments may vary from these exemplary detailsand still be contemplated to be within the scope of the presentdisclosure.

Additionally, some embodiments may be practiced in distributed computingenvironments where the machine-readable medium is stored on and orexecuted by more than one computer system. In addition, the informationtransferred between computer systems may either be pulled or pushedacross the communication medium connecting the computer systems.

Embodiments of the claimed subject matter include, but are not limitedto, various operations described herein. These operations may beperformed by hardware components, software, firmware, or a combinationthereof.

Although the operations of the methods herein are shown and described ina particular order, the order of the operations of each method may bealtered so that certain operations may be performed in an inverse orderor so that certain operation may be performed, at least in part,concurrently with other operations. In another embodiment, instructionsor sub-operations of distinct operations may be in an intermittent oralternating manner.

The above description of illustrated implementations of the invention,including what is described in the Abstract, is not intended to beexhaustive or to limit the invention to the precise forms disclosed.While specific implementations of, and examples for, the invention aredescribed herein for illustrative purposes, various equivalentmodifications are possible within the scope of the invention, as thoseskilled in the relevant art will recognize. The words “example” or“exemplary” are used herein to mean serving as an example, instance, orillustration. Any aspect or design described herein as “example” or“exemplary” is not necessarily to be construed as preferred oradvantageous over other aspects or designs. Rather, use of the words“example” or “exemplary” is intended to present concepts in a concretefashion. As used in this application, the term “or” is intended to meanan inclusive “or” rather than an exclusive “or”. That is, unlessspecified otherwise, or clear from context, “X includes A or B” isintended to mean any of the natural inclusive permutations. That is, ifX includes A; X includes B; or X includes both A and B, then “X includesA or B” is satisfied under any of the foregoing instances. In addition,the articles “a” and “an” as used in this application and the appendedclaims should generally be construed to mean “one or more” unlessspecified otherwise or clear from context to be directed to a singularform. Moreover, use of the term “an embodiment” or “one embodiment” or“an implementation” or “one implementation” throughout is not intendedto mean the same embodiment or implementation unless described as such.Furthermore, the terms “first,” “second,” “third,” “fourth,” etc. asused herein are meant as labels to distinguish among different elementsand may not necessarily have an ordinal meaning according to theirnumerical designation.

It will be appreciated that variants of the above-disclosed and otherfeatures and functions, or alternatives thereof, may be combined intomay other different systems or applications. Various presentlyunforeseen or unanticipated alternatives, modifications, variations, orimprovements therein may be subsequently made by those skilled in theart which are also intended to be encompassed by the following claims.The claims may encompass embodiments in hardware, software, or acombination thereof.

What is claimed is:
 1. A method comprising: receiving a request for atransportation plan for a user, wherein the request comprises a startingpoint and an ending point for a route; generating a set of potentialtransportation plans for the route; determining an impact of a subset ofplans in the set of potential transportation plans; estimating, by aprocessing device, a likelihood of acceptance of the subset of plans inthe set of potential transportation plans based on a personal preferencemodel for the user; selecting, by the processing device, an influencestrategy of the user based on a user model; and generating, by theprocessing device, a message recommending a selected plan from thesubset of plans for the user, wherein the message is generated based onthe selected influence strategy.
 2. The method of claim 1, furthercomprising: receiving an indication of that the user did not accept theselected plan; and updating the user model based on the user notaccepting the selected plan.
 3. The method of claim 1, whereindetermining the impact of the subset of plans comprises estimating theestimated travel time and the estimated fuel consumption comprisessimulating the potential transportation plans across a transportationsystem.
 4. The method of claim 1, further comprising generating the usermodel based on explicit and inferred data regarding the user.
 5. Themethod of claim 1, wherein generating the message comprises: identifyinga messaging template based on the selected influence strategy; andcompleting the messaging template based at least in part of a firstimpact measurement of the selected transportation plan.
 6. The method ofclaim 1, further comprising: estimating a measurement of change to atransportation system for the subset of plans; selecting the selectedplan based on the estimated measurement of change to the transportationsystem and the estimated likelihood of acceptance of the subset ofplans.
 7. The method of claim 1, further comprising providing thegenerated message to a user device through a user interface.
 8. A systemcomprising: a memory device; and a processing device operatively coupledto the memory device, wherein the processing device is to: receive arequest for a transportation plan for a user, wherein the requestcomprises a starting point and an ending point for a route; determine aselected transportation plan for the user; select an influence strategyof the user based on a user model associated with the user; and generatea message recommending the selected transportation plan for the user,wherein the message is generated based on the selected influencestrategy.
 9. The system of claim 8, wherein to determine the selectedtransportation plan, the processing device is further to: generate a setof potential transportation plans for the route; determine an impact ofa subset of plans in the set of potential transportation plans; andestimating, by a processing device, a likelihood of acceptance of thesubset of plans in the set of potential transportation plans based on apersonal preference model for the user, wherein the personal preferencemodel is based on at least one of publicly available sources, privatedata, surveys or user input.
 10. The system of claim 9, wherein todetermine the impact of the subset of plans, the processing device isfurther to estimate the estimated travel time and the estimated fuelconsumption comprises simulating the potential transportation plansacross a transportation system.
 11. The system of claim 8, wherein theprocessing device is further to: receive an indication of that the userdid not accept the selected plan; and update the user model based on theuser not accepting the selected plan.
 12. The system of claim 8, whereinthe processing device is further to generate the user model based onexplicit and inferred data regarding the user.
 13. The system of claim8, wherein to generate the message the processing device is further to:identify a messaging template based on the selected influence strategy;and complete the messaging template based at least in part of a firstimpact measurement of the selected transportation plan.
 14. The systemof claim 8, wherein the processing device is further to: estimate ameasurement of change to a transportation system for the subset ofplans; select the selected plan based on the estimated measurement ofchange to the transportation system and the estimated likelihood ofacceptance of the subset of plans.
 15. A non-transitorycomputer-readable medium having instructions stored thereon that, whenexecuted by a processing device, cause the processing device to: receivea request for a transportation plan for a user, wherein the requestcomprises a starting point and an ending point for a route; determine aselected transportation plan for the user; select an influence strategyof the user based on a user model associated with the user; and generatea message recommending the selected transportation plan for the user,wherein the message is generated based on the selected influencestrategy.
 16. The non-transitory computer-readable medium of claim 8,wherein to determine the selected transportation plan, the processingdevice is further to: generate a set of potential transportation plansfor the route; determine an impact of a subset of plans in the set ofpotential transportation plans; and estimating, by a processing device,a likelihood of acceptance of the subset of plans in the set ofpotential transportation plans based on a personal preference model forthe user.
 17. The non-transitory computer-readable medium of claim 9,wherein to determine the impact of the subset of plans, the processingdevice is further to estimate the estimated travel time and theestimated fuel consumption comprises simulating the potentialtransportation plans across a transportation system.
 18. Thenon-transitory computer-readable medium of claim 8, wherein theprocessing device is further to: receive an indication of that the userdid not accept the selected plan; and update the user model based on theuser not accepting the selected plan.
 19. The non-transitorycomputer-readable medium of claim 8, wherein to generate the message theprocessing device is further to: identify a messaging template based onthe selected influence strategy; and complete the messaging templatebased at least in part of a first impact measurement of the selectedtransportation plan.
 20. The non-transitory computer-readable medium ofclaim 8, wherein the processing device is further to: estimate ameasurement of change to a transportation system for the subset ofplans; select the selected plan based on the estimated measurement ofchange to the transportation system and the estimated likelihood ofacceptance of the subset of plans.